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- Author: Robert Blanchard
- Publisher: SAS Institute
- ISBN-10: 1642959154
- ISBN-13: 9781642959154
- Format: 19.1 x 23.5 x 0.8 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
Discover deep learning and computer vision with SAS!Deep Learning for Computer Vision with SAS(R) An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations.
Readers will learn how to:
- Define and understand deep learning
- Build models using deep learning techniques and SAS Viya
- Apply models to score (inference) new data
- Modify data for better analysis results
- Search the hyperparameter space of a deep learning model
- Leverage transfer learning using supervised and unsupervised methods
EXTRA 10 % discount with code: EXTRA
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- Author: Robert Blanchard
- Publisher: SAS Institute
- ISBN-10: 1642959154
- ISBN-13: 9781642959154
- Format: 19.1 x 23.5 x 0.8 cm, minkšti viršeliai
- Language: English English
Deep Learning for Computer Vision with SAS(R) An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations.
Readers will learn how to:
- Define and understand deep learning
- Build models using deep learning techniques and SAS Viya
- Apply models to score (inference) new data
- Modify data for better analysis results
- Search the hyperparameter space of a deep learning model
- Leverage transfer learning using supervised and unsupervised methods
Reviews